Mobile taxi booking application service’s continuance usage intention by users

Transportation Research Part D 57 (2017) 207–216

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Mobile taxi booking application service’s continuance usage intention by users Gooi Sai Wenga, Suhaiza Zailania, Mohammad Iranmaneshb, Sunghyup Sean Hyunc, a b c

MARK ⁎

Faculty of Business and Accountancy, University of Malaya, 50603 UM, Kuala Lumpur, Malaysia Graduate School of Business, University Sains Malaysia, 11800 USM, Penang, Malaysia School of Tourism, Hanyang University, 17 Haengdang-dong, Seongdonggu, Seoul 133-791, Republic of Korea

AR TI CLE I NF O

AB S T R A CT

Keywords: Taxi booking application Continuance usage intention Malaysia

The long-term development of a mobile booking taxi application service depends on the continued use of its passengers. The aim of this study is to investigate the determinants of the mobile taxi booking application service’s continuance intention, using the technology continuance theory by including the perceived risk and subjective norms. The data were collected by surveying 387 users of the mobile taxi application service. The data were analysed by applying the partial least squares technique. The analysis showed that the technology continuance theory has extensive power to explain the continuance intention to use the mobile booking taxi application. Subjective norms also have a significant effect on the attitude of mobile booking taxi application users which represents an important contribution to technology continuance theory extension. The theoretical and practical significances of the study have been discussed.

1. Introduction A taxi carries out an important task of offering personalised service in the urban transportation system. The disparity between the taxi supply and passenger demand is one of the challenges of running an effective taxi service these days (Shen et al., 2015). This makes it difficult for travellers to be picked up on time, and available taxis must waste lots of time to get customers, which worsens the existing traffic congestion and the air pollution problem. Mobile taxi booking (MTB) Apps have been developed in cities as a bridge to connect passengers and taxis (Shen et al., 2015), and this is to deal with the above dilemma. The passengers use the MTB App to request a ride. With an MTB App, passengers can search for available taxis around them and make an order. They fix their locations by GPS or typing the target location, by which drivers can easily reach them (Chan et al., 2016). The app sends the request to the nearest available driver who then either accepts or declines the trip. The MTB Apps, such as Uber and GrabCar, are varying the method in which passengers book taxis. With the fast outburst of the release of the new MTB Apps offering lower fares and a variety of payment methods, some top MTB Apps, such as GrabCar have revealed a drop in use (Pultan, 2016). Alternative MTB Apps, such as Uber, have been gradually gaining in popularity. As developers release more MTB Apps, users are now left with many opportunities to access them. It is very essential for the MTB App’s service providers to know how to keep their present users. It is also very beneficial for the MTB’s service providers to know how the users develop their continuance intent so that they can provide them with new MTB applications to satisfy their needs. There are many benefits of the MTB Apps for taxi drivers. MTB Apps may add to taxi drivers’ incomes if they are able to allow

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Corresponding author. E-mail addresses: [email protected] (G.S. Weng), [email protected] (S. Zailani), [email protected] (M. Iranmanesh), [email protected] (S.S. Hyun). http://dx.doi.org/10.1016/j.trd.2017.07.023

1361-9209/ © 2017 Published by Elsevier Ltd.

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more bookings with less empty cruising and reduce operating costs and, at the same time, it may boost the supply of taxis. There will be a more relaxed working atmosphere with noiseless data. Fewer misunderstandings will result between the dispatcher and the driver on booking messages compared with the conventional radio wave booking system. And more significantly, it will offer customers a great experience and, as a result, enhance their satisfaction and loyalty (Keong, 2015). In terms of the environment, MTB Apps may have a positive impact. This is because people may use public transportation more often if they are certain that they can later use a ride service. For example, someone might use a train to get to work rather than drive because they know that they can get home even if they miss the last train. Some people also use MTB Apps to get home or from public transportation, especially when the weather is rainy. Furthermore, most of the MTB companies require cars to be relatively new, which may be less polluting than the vehicles owned by the general population. In addition, most of the MTB companies provide incentives for fuel-efficiency. Given the benefits of the MTB Apps for passengers, taxi drivers and environment, the expansion of their number, and the fierce competition amongst their providers, academics and experts strive to explore the factors that affect their use (Keong, 2015). The early acceptance of the MTB Apps does not guarantee continuation afterwards since passengers might re-evaluate their initial decision or experience emotional motivation shifts after their initial acceptance (Bhattacherjee, 2001; Bhattacherjee and Premkumar, 2004). Nevertheless, despite some researchers having surveyed the MTB pre-adoption behaviour of potential passengers (Keong, 2015), studies on continuance intention are still lacking. Without a clear understanding of the adoption behaviour of MTB’s users over time, decision makers and MTB’s service providers will not be able to improve the usage of MTB. So, the current study has carried out a research framework of the MTB App’s continuance intent which is based on the theory of technology continuance by Liao et al. (2009). In addition, there is growing empirical evidence that perceived risk is a critical determinant of attitude towards technology usage (Im et al., 2008) and the subjective norm is an important factor amongst Asian consumers (Weng and de Run, 2013). These studies have identified the importance of the variables in explaining the attitude and continuance intent to use. Thus, the current study attempts to enrich the Technology Continuance Theory (TCT) by including perceived risk and subjective norms as predictor variables to improve our ability to understand the MTB App’s continuance intention to use formation. In addition, this study changes the attention from the initial adoption to the continued use of the MTB App. The remainder of this paper is organised as follows. The second section conceptualises the framework of the study by using the TCT and thereby derives the hypotheses. It is followed by the third section which outlines the methodology. The fourth section analyses the data and presents the outcomes. Finally, the fifth section talks about the findings, gives the theoretical and practical implications, as well as the limits of the current study and the recommendations for future ones. 2. Model conceptualisation and hypothesis development This study aims to examine the continuance intent of the MTB App’s users. Three models have been used as the general theories in earlier studies and they include: the Technology Acceptance Model (TAM), Expectation Confirmation Model (ECM), and Cognitive Model (COGM). The TAM was developed by Davis (1989) on the basis of the theory of reasoned action (TRA) (Fishbein and Ajzen, 1975). Davis (1989) claimed that the two external factors that can motivate individuals’ behavioural intent to use an information system (IS) are the perceived usefulness and perceived ease of use. The TAM has been applied to investigate the continuance intention to use (Karahanna and Straub, 1999; Taylor and Todd, 1995). The TAM, with its focal point on the initial approval of an IS, propounds that system use is in a straight line decided by the behavioural intent to use, and it is in turn aggravated by the user’s approach as regards to the system’s use (Liao et al., 2009). The inability to see the influence of external variables and barriers to technology acceptance is again an additional limitation of the TAM (Yarbrough and Smith, 2007). Therefore, several earlier studies have established that the TAM can have an improved illustrative power when used with extra external factors (Hu et al., 1999; Lee et al., 2003; Lin et al., 2012). The ECM (Bhattacherjee, 2001) has recently been employed to explicate the users’ behaviour to continually use an IS. The ECM was introduced to explain users’ continuance intention to use a system. This shows that meeting users’ needs is an influential parameter impacting on their constant use. The ECM has emerged from the literature of users’ behaviour and has been mixed with empirical and theoretical outcomes of studies on the IS usage in order to form the IS continuance model (Bhattacherjee, 2001). According to the model, it is suggested that customers’ continuance intent is rooted in their satisfaction with using the IS and the perceived usefulness of the continued IS use. However, confirmation of the expectation from prior use of the IS and the perceived usefulness affect the users’ satisfaction. Unlike the TAM, the ECM pays attention to the factors that influence continuance and retention because an IS’s viability and achievement are affected by the continuous use rather than the first-time usage (Bhattacherjee, 2001; Liao et al., 2009). COGM was developed by Oliver (1980) who proposed both attitude and satisfaction as determinants of behavioural intention. The TCT has been used to describe the continuance usage intent of the MTB App amongst users in this study. The TCT was recommended as an improved model for the IS continuance that is appropriate for the total life cycle of acceptance (Liao et al., 2009). Three models, TAM, ECM, and COGM, with their six constructs, which are: confirmations, satisfaction, perceived usefulness, perceived ease of use, and attitude, have been synthesised to create a condensed model. The major strong point of the TCT is that satisfaction and attitude are merged into one single continuance model whilst keeping the well-determined variables of perceived ease of use and usefulness as the first level of antecedent (Liao et al., 2009). Compared with the ECM, TAM, and COGM, the TCT was favoured for this research because of its explaining power for the adoption of the whole life cycle. According to Liao et al. (2009), unlike the ECM, TAM, and COGM models, the TCT qualitatively and quantitatively provides a considerable improvement in order to explain the consumers’ attitudes at different stages of confirmation. The TCT quantitatively represents a powerful explanation both 208

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Fig. 1. Proposed extended TCT model for MTB App continuance intention.

for continuance intent and satisfaction. As for the qualitative point of view, the TCT contributes theoretically in that it merges attitude and satisfaction, into one continuance model. Previous research results have suggested that the perceived risk and subjective norms are significant constructs of attitude and continuance intention to use (e.g., Liao et al., 2007) and such constructs could be added to the TCT. For that reason, the current study model has included perceived risk and subjective norms into the original TCT to better predict the attitude and continuance intention to use the MTB App. The modified TCT method is shown in Fig. 1. The given model involves the new constructs (subjective norms and perceived risk) as well as original variables. Attitude refers to “the degree of a person’s positive or negative outlook as regards the performance of a target behaviour” (Davis, 1989). According to the TAM, consumers’ attitudes can be used to determine the users’ behaviour towards MTB use. The literature suggests that the attitude has a significant impact on the IS use and acceptance (Liao et al., 2009; Lee, 2010). As people show a positive perception towards a new system and technology, they are more enthusiastic to take advantage of it (Lee, 2010). Therefore, it is believed that when users have positive attitudes towards an MTB App, they will have a stronger interest to use it. These hypotheses have then been formed: H1. The attitude of the MTB App’s users towards using the MTB App is positively related to their continued MTB App continuance usage intention. User satisfaction is concerned with his or her examination of an IS, which vividly describes the emotional response to the IS (Ives et al., 1983). A great number of researchers have studied consumer satisfaction which shows the IT effectiveness from the users’ points of view (DeLone and McLean, 1992) and also indicates the success of the IT acceptance in a compulsory environment (Oliver, 1993). The ECM maintains that the continuance intention of users is shaped by their satisfaction of using the IS as well as their perceived usefulness of continued IS use. As Oliver (1993) and Lee (2010) believe, users being satisfied with the IS use plays a vital role in forming the IS continuance intent. In the context of the MTB App, the users’ satisfaction with the MTB App may derive their continuance intention to use. Even though some past studies have considered attitude and satisfaction as being synonymous with each other (Bolton and Drew, 1991), most agree that the two are different from a conceptual point of view. Whilst attitude is more stable and goes beyond prior experiences, satisfaction seems to be considered as a momentary experience-related factor (Liao et al., 2009). Attitude is a belief emerging out of the personal evaluation of a system, service, and product whilst satisfaction is an evaluation occurring after purchasing a service or product (Venkatesh and Davis, 2000). Satisfaction has been analysed in depth in the literature and has been found to play a critical role in predicting consumers’ attitudes and continuous behaviors (Gilani et al., 2017; Iranmanesh et al., 2017). Even though satisfaction and attitude are regarded as two distinct constructs, their effects on user behaviour can possibly be combined. For that reason, the following hypotheses have been developed: H2. Satisfaction of the MTB App’s users with the MTB App is positively related to their continued MTB App continuance usage intention. H3. Satisfaction amongst the MTB App’s users is positively related to their attitudes towards using the MTB App. 209

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Perceived ease of use refers to the degree to which a person accepts that using a technology will be free of effort (Davis, 1989); on the other hand, perceived usefulness is concerned with the perception that people have about how much a product or technology helps and facilitates caring out work. The literature indicates that the perceived ease of use and perceived usefulness seem to be particularly vital measures of the users’ intent towards using the system (Davis, 1989; Lee, 2010; Venkatesh and Davis, 1996). A great deal of research has shown that these two factors jointly impact on the use and acceptance of a certain type of service or product. The perceived usefulness has been considered as the most vital factor amongst the TAM variables (Davis, 1989; Zailani et al., 2014). In the context of users’ attitudes towards using the MTB App, there is a lack of study on testing the applicability of the TAM. Shen et al. (2015) investigated the users’ adoption of mobile Apps. Their results showed that the perceived usefulness and perceived ease of use have significant effects on the users’ attitudes towards using mobile Apps. Users will always want to keep using a particular App that can help them improve productivity (Bhattacherjee, 2001; Bhattacherjee and Premkumar, 2004). Moreover, according to Roca and Gagné (2008) and Lee (2010), there is a positive relationship between the intention to use and the perceived usefulness of a new product. Users need to see the MTB App as a useful tool for taking a taxi in an easier and a faster way with a lower fare. Moreover, the users need to feel that the MTB App is quite easy to use. The MTB APP must be easy to use to motivate users to use it (Peng et al., 2014). The TRA submits that the perceived usefulness and perceived ease of use perceptions can influence the user’s attitude and intent to use. Thus, we posit that: H4. Perceived usefulness amongst the MTB App’s users is positively related to their MTB App continuance usage intention. H5. Perceived usefulness amongst the MTB App’s users is positively related to their attitudes towards using the MTB App. H6. Perceived ease of use amongst the MTB App’s users is positively related to their attitudes towards using the MTB App. Subjective norm refers to ‘‘the perceived social pressure to perform or not to perform the behaviour” (Ajzen, 1991). In other words, the subjective norm is linked with the normative views about the expectancy from some other fellow. Hsu and Lu (2004) suggested that subjective norms have a great influence on the intentions and attitudes of individuals concerning certain types of behaviour. Many users decide to make use of an MBT App if it is also used by their friends and is recommended to them. Applications of mobiles seem to be useful if they appear to be user-friendly. The previous studies have experimentally indicated a strong and positive relationship between perceived ease of use and usefulness (Yang, 2005; Pai and Huang, 2011; Son et al., 2012). Reviewing prior studies on the TAM, Hung et al. (2005) showed that of 39 studies, 30 articles supported that the perceived ease of use has a positive impact on the perceived usefulness. Considering the given results, the hypothesis of the study is as follows: H7. Subjective norm is positively related to the MTB App’s users’ continuance usage intention. H8. Subjective norm is positively related to the MTB App’s users’ attitudes towards using the MTB App. A Mobile App is generally perceived as more useful if it is easy to use. Earlier studies have revealed strong experimental support for a positive correlation between perceived usefulness and perceived ease of use (Yang, 2005; Pai and Huang, 2011; Son et al., 2012; Shiau and Chau, 2016). Hung et al. (2005) considered earlier research articles on the TAM and revealed that amongst 39 articles, more than 30 claimed that the perceived ease of use amongst users positively affects the perceived usefulness. Based on the above results, the hypothesis has been presented as follows: H9. Perceived ease of use amongst the MTB App’s users is positively related to their perceived usefulness of the MTB App. Risk is defined with respect to the consequences and uncertainty related with consumers’ decisions (Bauer, 1960). Some previous studies on this have explained that risk is the perceived uncertainly in a purchase situation. If a technology fails to deliver its likely outcome, it will result in a loss to the user. So, perceived risk affects people’s confidence in the perceived usefulness of a technology (Im et al., 2008). The MBT App involves great uncertainly and risk including drivers being not safe, risk in the mobile payment, and risk of not receiving a taxi or receiving it after a long delay. Passengers’ perceptions of risk in using the MTB App may negatively influence their attitudes towards using the App (Peng et al., 2014). As such, the following hypothesis has been developed: H10. Perceived risk amongst the MTB App’s users is negatively related to their attitudes towards using the MTB App. The previous studies have underpinned a relationship between IT satisfaction and perceived usefulness (Bhattacherjee, 2001; Li and Liu, 2014). An individual who finds a particular IT useful is more likely to be contented with it than a person that does not (Lee, 2010). Those types of IT that give their users the perception of being more useful are more likely to be utilised (Davis, 1989). Prior research has verified that the perceived usefulness has a significant effect on a user’s satisfaction of a mobile application (Ghazal et al., 2016). Therefore, the following hypothesis has been developed: H11. Perceived usefulness of the MTB App amongst the MTB App’s users is positively related to their satisfaction with the MTB App. The ECM suggests that there are two factors useful in examining user satisfaction: the IS expectation and the expectation confirmation after real use. Expectation forms the baseline stage whilst confirmation is assessed by users to determine the conclusive satisfaction or response. It is believed that there is a positive connection between confirmation and satisfaction of IS use because it 210

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deals with the expected benefits that a person takes from using the IS while disconfirmation refers to the situation where expectations are not achieved (Bhattacherjee, 2001). Conducting a study on IT adoption’s behaviour life cycle, Liao et al. (2009) argued that after experiencing the use over time, the IS perceived performance is compared to the pre-adoption anticipation. The outcomes of the disconfirmation or confirmation could determine the amount of users’ satisfaction. Accordingly, the examination of the anticipation of the prior IS use and its perceived usefulness could, to a large extent, impact on the users’ satisfaction. The expectations’ validation indicates that if users gain the expected advantages by experiencing the use of the MTB App, their satisfaction will be affected positively. Thus, the following hypothesis has been presented: H12. Confirmation of the expectation of the MTB App’s users is positively related to their satisfaction with the MTB App. Confirmation is considered as a cognitive notion referring to the degree that the actual use of the IS reflects the expected use of the IS. The confirmation is, in fact, coming from the prior use of the IS (Bhattacherjee, 2001). As cognitive notions (e.g., perceived ease of use and usefulness) are related in the IS acceptance situations (Davis, 1989), other cognitive notions (e.g., perceived usefulness and confirmation) are supposed to be related in the IS continuance situations (Bhattacherjee, 2001). Confirmation experience could be used to adjust the IS perceived usefulness, especially when doubt and uncertainty about what to expect from the IS usage overshadow the consumers’ primary perceived usefulness (Bhattacherjee, 2001). For example, users might not have a positive usefulness of a new technology, mainly because they do not know exactly what to expect from its usage. However, they are likely to accept the new technology with the purpose of making their experience of using it as a platform to shape more perceptions. Even though at the initial level of using the IS, there is a low perceived usefulness, such a perceived usefulness might be modified based on the outcomes of the confirmation experiences as users acknowledge that their primary understanding could unrealistically appear low. To be more specific, confirmation and disconfirmation, respectively, increases and lessens the degree of the perceived usefulness. Therefore, the hypothesis of the study is as follows: H13. Confirmation of the expectations of the MTB App’s users is positively related to their perceived usefulness of the MTB App. 3. Research methodology 3.1. Measure of constructs Containing 40 items, the questionnaire of the study was comprised of two parts: the first part asked demographic questions about the participant, and the second part was concerned with items assessing the TCT’s theoretical constructs. Demographic information included gender, age, race, occupational status, academic qualification, monthly family income, average monthly transportation cost, and length of experience in using the MBT App. To ensure the content validity, the survey items were derived from those used in previous studies. The scale of the perceived usefulness and perceived ease of use were adapted from Venkatesh and Davis (1996). The confirmation, satisfaction, and intention items were adapted from Bhattacherjee (2001). The items of the perceived risk and subjective norms were adapted from Peng et al. (2014). Finally, attitude was determined using a four-item scale adapted from Taylor and Todd (1995). A five-point Likert scale was used to measure each item. The items of the study have been provided in Table 1. 3.2. Procedures and data collection According to a Hand Phone User Survey conducted by the Malaysian Communications and Multimedia Commission, around 53.4% of mobile users in Malaysia have a Smartphone (MCMC, 2014). This indicates that the majority of the Malaysian people have access to the MTB App. The study was conducted using convenience sampling in the central parts of Kuala Lumpur in Malaysia due to the fact that the population of the MTB App’s users was not available and it was impossible to select our sampling element from the population, directly. A convenience sample of 480 citizens aged above 18 years was approached and handed a questionnaire. A cover letter explained the purpose of the survey. Data collection was conducted within one month. A filtering question at the beginning of the survey questionnaire ensured that the respondents had experience with the MTB App. 480 questionnaires were distributed, and 418 responses were received. Out of the 418 responses, 3 were incomplete, 28 respondents mentioned that they had no experience with the MTB App. This left a total of 387 valid responses, or an 80.6 response rate. Freedman (2015, p. 4) suggested that “if the response rate is high (most interviews are completed), non-response bias is minimal”. Therefore, non-response bias is not an issue in this paper. 3.3. Statistical methods The partial least squares (PLS) method of structural equation modelling (SEM) employing SmartPLS Version 3.0 was utilised to evaluate the research model. This statistical technique was used to measure the appropriateness in order to present a valid analysis of the complicated model (Hair et al., 2011). The two-step approach was employed in order to analyse the collected data (see Zailani et al., 2015; Nikbin et al., 2015; Yusof et al., 2016). At the first step, the measurement model was evaluated whilst the second step was concerned with finding the possible relationship amongst the different constructs. Before identifying the existing relationships in the model, employing this special statistical technique, the present study made an attempt to measure the reliability and validity of the measurements. 211

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Table 1 Measurement model evaluation. Constructs

Items

Factor loadings

CR

AVE

Confirmation (CON)

My experience with using MTB app was better than what I expected The service level provide by MTB app was better than what I expected The information provide by MTB app was better than what I expected The access to taxi fares was easier that what I expected Overall, most of my expectations from using MTB app were confirmed MTB app is a useful tool for taking a taxi MTB app is more convenience for taking a taxi MTB app can help me to take a taxi easier MTB app can help me to take a taxi faster MTB app can help me to take a taxi with lower fare Overall, I find MTB app useful in booking taxi Interaction with MTB app does not require a lot of mental effort I find MTB app to be easy to use Learning MTB app would be easy for me MTB app is flexible to interact with Overall, I find MTB app easy to use The drivers of Taxi apps are not safe I think there is risk in mobile payment when using Taxi app My overall experience of MTB app use was: very satisfied My overall experience of MTB app use was: very pleased My overall experience of MTB app use was: very contented My overall experience of MTB app use was: absolutely delighted Using MTB app rather than taking taxi in conventional way would be a good idea Using MTB app rather than taking taxi in conventional way would be a wise idea I like the idea of using MTB app rather than taking taxi in conventional way Using Taxi apps would be a pleasant experience My family thinks that I should use MTB app My friends think that I should use MTB app My colleagues think that I should use MTB app I intend to continue using MTB app rather than discontinue its use My intentions are to continue using MTB app than taking taxi in conventional way If I could, I would like to continue using MTB app as much as possible

0.860 0.894 0.849 0.855 0.840 0.869 0.896 0.942 0.883 0.618 0.884 0.814 0.891 0.879 0.893 0.898 0.854 0.897 0.902 0.932 0.933 0.906 0.887 0.911 0.889 0.883 0.666 0.941 0.920 0.923 0.923 0.920

0.934

0.739

0.941

0.731

0.943

0.767

0.868

0.767

0.956

0.844

0.940

0.797

0.885

0.725

0.945

0.851

Perceived Usefulness (PU)

Perceived Ease of Use (PEU)

Perceived Risk (PR) Satisfaction (SAT)

Attitude (ATT)

Subjective Norms (SN)

Continuance Intention (CI)

CR = Composite Reliability; AVE = Average Variance Extracted.

4. Analysis 4.1. Sample characteristics Male respondents comprised 45.6% of the total respondents whereas female respondents comprised 54.6% of them. In terms of age, the majority of the respondents (77.7%) were under 35 years old. A total of 151 (39.0%) of the respondents were less than 29 years old, 118 (30.5%) respondents were between 30 and 39 years old, 66 (17.1%) respondents were between 40 and 49 years old, and 52 (13.4%) respondents were above 50 years old. Approximately 190 (49.1%) respondents were Malay, 131 (33.9%) were Chinese, 53 (13.7%) were Indian, and 13 (3.3%) were other races. In terms of the educational attainment of the respondents, 42.6% had a bachelor’s degree, 24.3% had a diploma, 16.3% were schooling, 12.1% had a master’s degree, and 4.7% had a PhD. All of the respondents had experience with the MTB App. The length of experience of 39.3% of the respondents with the MTB App was less than six month whereas 60.7% had usage experience of above six months.

4.2. Measurement model results The reflective constructs were examined in terms of reliability and validity (see Kurniawan et al., 2017; Yusof et al., 2017; Shaharudin et al., 2017). Composite reliability (CR) is equivalent to Cronbach’s alpha and is measured in connection with internal reliability. Table 1 shows that the CR of all the constructs was above 0.7, which satisfied the rule of thumb in Hair et al. (2013). The acceptance of items with a minimum loading of 0.6 was suggested by Hair et al. (2010). The reliability of individual items was logically judged, due to the fact that all the scales reported loadings that exceeded 0.6. The average variance extracted (AVE) was used to evaluate the convergent validity; this value exceeded 0.5 in all of the constructs. This result implies that the convergent validity of these constructs was adequate (Fornell and Larcker, 1981). Two procedures were used to determine the discriminated validity in the constructs (see Zailani et al., 2016; Zainuddin et al., 2017). The first to be examined was the indicator of cross-loadings of which the opposing construct did not go above any indicator load (Hair et al., 2012). Next, the value of the inter-correlations between the construct and other model constructs should have been surpassed by the square root of the AVE of a single construct (Table 2). Both analyses confirmed the discriminated validity of all the constructs. Table 2 shows that the mean of all the variables were greater than three except for the perceived risk. 212

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Table 2 Discriminant validity coefficients.

CON PU PEU PR SAT ATT SN CI

Mean

St.D

CON

PU

PEU

PR

SAT

ATT

SN

CI

3.545 3.872 3.878 2.936 3.511 3.753 3.440 3.733

0.757 0.795 0.703 0.720 0.696 0.778 0.715 0.797

0.860 0.692 0.665 0.326 0.711 0.575 0.429 0.627

0.855 0.778 0.427 0.659 0.760 0.564 0.737

0.876 0.422 0.652 0.652 0.592 0.692

0.876 0.476 0.408 0.298 0.416

0.919 0.652 0.473 0.706

0.893 0.558 0.782

0.851 0.532

0.922

St.D = Standard Deviation. Diagonal terms (in bold) are square roots of the AVE.

4.3. Structural model results The structural model was afterwards assessed with the suitable results of the measurement model. The Standardised Root Mean Square Residual (SRMR) of the model was 0.05, which was an appropriate fit assuming the usual cut-off of 0.08 (Henseler, 2014). The predictive accuracy of the model was assessed in terms of the fraction of difference described. The results show that the model was able to describe 55.9%, 63.5%, and 70.1% of the differences in satisfaction, attitude, and continuance intention, respectively. In addition to estimating the R2 magnitude, the Stone–Geisser Q2 (cross-validated redundancy) value was calculated to measure the predictive significance according to a blindfolding method performed in the PLS (Stone, 1974; Geisser, 1975). The present research obtained a 0.516, which was considerably higher than zero for the average cross-validated redundancy (Chin, 2010). As a result, the model displayed up to a standard fit and high predictive significance. Non-parametric bootstrapping was used (Wetzels et al., 2009) with 2000 replications to test the structural model. Table 3 presents the structural model that resulted from the PLS analysis. All the paths were significant with the exception of three (H6, H7, and H10). 5. Discussion The aim of the study was to examine the continuance usage intention of the MTB App using the TCT model. One of the strengths of the TCT is that it synthesises attitude and satisfaction, the two central constructs, into one continuance model, and it preserves perceived usefulness and perceived ease of use as first-level antecedents at the same time. The potential effects of subjective norms and perceived risk were also investigated to enrich the TCT. With the exception of one hypothesis, all the hypotheses in the TCT model were supported. Although the impacts of the perceived risk on the attitude and the subjective norms on the continuance usage intention were rejected, the results support the effects of the subjective norms on the attitude towards using the MTB App. This result shows that the TCT model has a high explanatory power to explain the continuance intention to the use of the users towards the MTB App. The TCT model can also be extended by considering the effect of the subjective norms on the attitude. The confirmation and perceived ease of use were the antecedents to the perceived usefulness. The effect of the perceived ease of Table 3 Path coefficient and hypothesis testing. Hypothesis H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13

Relationship

Path coefficient

Decision

+

0.413

***

Supported

+

0.269***

Supported

+

0.227**

Supported

+

0.217**

Supported

+

0.504***

Supported

ATT → CI SAT → CI SAT → ATT PU → CI PU → ATT +

0.007

Not Supported

+

0.052

Not Supported

+

0.152**

Supported

0.570***

Supported

PEU → ATT SN → CI SN → ATT +

PEU→ PU −

0.036

Not Supported

+

0.320***

Supported

+

0.490***

Supported

+

0.313***

Supported

PR → ATT PU → SAT CON → SAT CON → PU

** p < 0.01. *** p < 0.001.

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use on the perceived usefulness (β = 0.570, p < 0.001) was higher than the effect of confirmation (β = 0.313, p < 0.001) and this absolutely agreed with the findings of Chen et al. (2014), Lee (2010), and Lin et al. (2005). It suggests that although the confirmation of the MTB App’s users’ expectation after actual use has an important effect on forming their perceived usefulness of this App, the software is easy to use plays a high role in the perceived usefulness of the MTB App in the post-usage stage. As such, the perceived ease of use is a basic requirement for the MTB App design. From this perspective, the MTB App’s service providers should develop a user friendly App and increase their effort to meet the expectations of their users. Additionally, in line with the previous studies by Hossain and Quaddus (2013), Bhattacherjee (2001), Son et al. (2012), and Li and Liu (2014), the results reveal the important effects of confirmation and perceived usefulness on satisfaction. Therefore, meeting the expectations of the MTB App’s users and ensuring that the MTB App is useful are important considerations for the MTB App’s service providers. Thus, the MTB App’s service providers should foster a close relationship with the App’s users to understand their expectations and extend the usefulness of the MTB App to satisfy their users. In the planning and development of MTB Apps, software developers should pay attention to practical functions and extend key features that are frequently required. On the marketing side, MTB companies should accentuate the full functionality of their Apps to cater efficiently to the different needs and expectations of the users. Li and Liu (2014) investigated users’ continuance intention in the context of online travel services, and the perceived usefulness and confirmation were found to explain 23.7% of user satisfaction. This research investigated users’ continuance intention with respect to online travel services and found that the perceived usefulness and confirmation explained 55.9% of user satisfaction. Online travel services can be used to acquire information for the purpose of making travel decisions or for hedonic and sociopsychological reasons, such as collecting pictures for fun, sharing travel information, and obtaining knowledge for social presence. Whereas, the MTB App is single purpose oriented and is used to book a taxi. Thus, there might be other constructs that influence the online travel service users’ satisfaction and usage decisions, such as perceived ease of use and perceived enjoyment. This can help clarify why the perceived usefulness and confirmation explained a higher variance of the construct of satisfaction in the context of the MTB App. Although perceived usefulness (β = 0.504, p < 0.001), subjective norms (β = 0.152, p < 0.01), and satisfaction (β = 0.227, p < 0.01) had significant effects on attitude, the effect of the perceived ease of use (β = 0.007, p > 0.001) and the perceived risk (β = 0.036, p > 0.05) on attitude were not supported. As the MTB App’s users expand more knowledge with the MTB App and their perceived usefulness is formed, using this App will become a routine for them. Therefore, the MTB App’s users will become familiar with its features, so that the ease of use will lose its importance in their attitudes towards using the App. Furthermore, difficulty in using MTB Apps is becoming of less concern, as they are increasingly user-friendly. As such, the insignificant impact of the perceived ease of use on the attitude of the MTB App’s users is predictable. In addition, if the MTB App’s users are satisfied with the App, and their families and friends have positive views on using the App for booking taxis, they will find the MTB App as a safe way for booking taxis and perceived risk will not be a concern. The mean value of the perceived risk in this study was low and confirmed that the MTB App’s users perceived a low risk with using the MTB App. In addition, since safety is a must have in a taxi service, its presence will not affect the users’ satisfaction. However, it is possible that its absence will negatively affect users’ satisfaction. This result implies that users of the MTB App will have positive attitudes towards using this App when they perceive that using the MTB App will help them to book taxis easier and faster, they perceive social pressure to use this App for booking taxis, and they are satisfied with their postusage experience of the MTB App. The significant effect of the subjective norms on the users’ attitudes implies that users are likely to refer to the opinions of their friends, family members, and colleagues and may favour these sources over mass media reports and expert opinions. The management of the MTB App may opt to use a positive word-of-mouth strategy to improve the positive attitude of their users towards using the MTB App. They may first need to consider how to facilitate a positive experience for their existing users, in order to retain their future acceptance, rather than solely rely on mass media (Hsu et al., 2014). The results also confirm the impacts of the perceived usefulness, satisfaction and attitude on the continuance intention to use the MTB Apps. This finding is consistent with that of Liao et al. (2009) and Venkatesh and Davis (1996). Liao et al. (2009) investigated users’ continuance intention in the context of an e-learning system and the perceived usefulness, satisfaction and attitude were found to have a significant effect on the users’ continuance intention. Once passengers find the MTB Apps to be useful, they will intend to continue to use the MTB Apps to book taxis. However, users dissatisfied with the MTB App’s services may stop using it, despite having a positive perception with regards to its usefulness. The results of the study show that attitude is the strongest predictor of the users’ continuance intention. Lee (2010) stated that attitude is based solely on cognitive beliefs (e.g., usefulness and ease of use) formed potentially via second-hand information referred by others, the media, advertising, or other sources. As such, from the marketing perspective, in addition to giving importance to the practical functions of their Apps, the MTB companies need to invest in advertising. The direct effect of subjective norms on continuance intention to use is not supported. In the adopting stage, social pressure may have an effect on the individuals to test the MTB App. After actual use, the MTB App’s users’ perceived usefulness, satisfaction, and attitudes will determine their continuance intention to use the App for booking a taxi. 6. Implications Several academic implications have been provided in the study. First, the TCT model, based on the empirical results, can explain not only the continuance intention to use the MTB App, but also the attitudes, satisfaction, and perceived usefulness of users towards the technology. This finding indicates that the TCT is a powerful model to explain the attitude, satisfaction, and continuance intention to use the App. Second, this study is one of the first attempts to explore the determinants of the MTB App’s users’ continuance intention to use this App. Previous research also employed the The TCT to explore the post-adoption behaviour in different domains, 214

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such as technology in hospitals, online games, and e-learning (Gilani et al., 2017; Hsu et al., 2014; Lee, 2010), and the current study confirm the generalisability and explanatory power of the TCT in the MTB App context. We arrived at the conclusion that the TCT is a good theory to investigate different post-adoption behaviours. Third, the significant effects of the subjective norms on attitude represent an important contribution to the theory by extending the TCT. To yield a more comprehensive understanding of the postadoption behaviours, future studies should consider subjective norms in addition to the constructs of TCT. The present study also has implications for future MTB research. Since confirmation is the main antecedent to satisfaction, future research may explore what factors influence this factor. Apart from its theoretical implications, this research also provides recommendations for the MTB App’s service providers. Gaining a thorough understanding of the determinants of the continuance intention of users towards the MTB App’s use will help the App’s service providers to grasp the factors that result in continued the MTB App use. With such understanding, the MTB App’s service providers can improve their App to ensure its continued use after the first adoption. Since, satisfaction, attitude, and perceived usefulness are critical to the continuance usage of the MTB App, the providers of this App should give special notice to the antecedents of the users’ satisfaction, attitude, and perceived usefulness. The MTB App’s service providers can increase the passengers’ beliefs of how the MTB App can enhance their convenience when taking a taxi (perceived usefulness) and, consequently, satisfy them by developing an App that provides the expectations of the users (confirmation) with a good user interface (perceived ease of use) consistency. The MTB APP’s service providers should realise the importance of the perceived usefulness of the MTB APP in the postadoption stage. The results show that the users’ perceived usefulness can not only enhance the continuance intention of the MTB App’s users but also influence their satisfaction and attitudes towards using the MTB App. In addition, the MTB App’s service providers must improve the attitude of the passengers regarding using taxi Apps rather than taking taxis in the conventional way; therefore, perceived usefulness and subjective norms should be considered as important factors during the MTB App’s design and publicity. However, the results show that the perceived risk is not a significant determinant of attitude towards using the MTB App. Thus, the MTB App’s providers should enhance the attitude of the users towards the MTB app by improving the perception of usefulness and through the subjective norms and not the users’ perception of risk. Furthermore, when the expectations of the online travel service users are met, user satisfaction is generated. Therefore, the MTB App’s service providers should try to meet the needs of their users, thus generating a cycle of satisfaction and taxi service improvement. The MTB App’s service providers can use this knowledge to promote the continued use of the App. 7. Limitations and future studies This study has certain limits that should be measured before we generalise its results. The present study centres on the continuous intent as the dependent variable. Consequently, more research is needed to employ a longitude design and follow the consumers’ frequent behaviours to be able to get a fuller understanding of the MTB App’s continued usage. Furthermore, following previous studies which used the TCT, the linear relationships amongst the variables were investigated. The potential non-linear relationships amongst the variables can be investigated in future studies. Moreover, the current study was conducted in Malaysia which limits the findings’ generalisability strength to other communities. It is also recommended that the differences and similarities across communities be studied. In addition, it seems to be interesting to investigate why some MTB Apps are more popular in some countries such as Hailo in Singapore and Uber in Malaysia. Moreover, future research should investigate the determinants of the confirmation, perceived ease of use, and perceived usefulness of the MTB App. 8. Conclusion The MTB App has the potential to transform the ways that the passengers find and take taxis into a more convenient way. However, the continued usage of this App is an important issue which has received less attention in previous studies. Therefore, the present study has used the TCT to investigate the factors that motivate passengers to use the Taxi App, continually. 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